Efficient Game Theory Based Resource Allocation and Cluster Based ANT Optimization for IoT based Cognitive Ratio Networks

In recent times, many real time applications are developed using IoT based mobile wireless communication which increased the demand. Cognitive radio technology supplies a platform for channel sharing between licensed and unlicensed users. To improve the sensing of users, spectral diversity cooperative channel sensing is used. In this research, a newer model is introduced to improve spectrum utilization and efficiency which is Game theory-based resource allocation and Cluster based Ant Optimization (GTR-CAO). This method is sub-divided into three sections namely game theory for resource allocation, clustering with data aggregation and multi-objective ant optimization for best path finding. Simulation experiments are handled using MATLAB. GTR-CAO is compared with three state such as distributed sequential coalition formation (DSCF), Stochastic Stackelberg Game Theory (SSGT), and fair multichannel assignment scheme (FMCA) in terms of throughput, resource utilization, and energy consumption. As a result, the proposed GTR-CAO achieves better performance compared with the earlier works.